Data Engineer-NY, SLC, TX, UT, Dallas, New York City , USA
Data Engineer-NY, SLC, TX, UT, Dallas, New York City , USA
Employer Active
Job Alert
You will be updated with latest job alerts via email
Share
Job Alert
You will be updated with latest job alerts via email
Job Description
Position: Senior Data Engineer Time Series Systems
We are looking for an experienced Senior Data Engineer to lead the design and development of high-performance scalable data infrastructure for large-scale time series workloads. This role is ideal for someone passionate about building robust systems that power real-time analytics and machine learning.
Key Responsibilities:
-
Design build and optimize data pipelines to process large volumes of time series data efficiently
-
Develop scalable data infrastructure using time series-focused technologies such as KDB TimeSet or Kronos
-
Create robust ingestion and transformation workflows to handle both real-time and historical datasets
-
Integrate time series systems with Python-based ML pipelines to support training and inference workflows
-
Collaborate closely with data scientists and ML engineers to ensure high-quality accessible data for experimentation and production
-
Design data models and schemas tailored for time series use cases supporting efficient downsampling indexing and aggregation
-
Monitor and optimize systems for performance reliability and scalability
-
Establish best practices in data governance lineage and observability within large-scale environments
-
Mentor junior team members in distributed processing data architecture and real-time systems
-
Work cross-functionally with product infrastructure and engineering teams to align data capabilities with business objectives
Qualifications:
-
5 years of experience in data engineering with a strong focus on large-scale and high-throughput systems
-
Hands-on experience with time series databases like KDB TimeSet or Kronos
-
Proven track record building batch and streaming data pipelines using technologies such as Apache Kafka Spark Flink or AWS Glue
-
Proficiency in Python with experience integrating data pipelines into ML workflows using libraries like pandas NumPy scikit-learn or PyTorch
-
Expertise in designing efficient data models and partitioning strategies for time series data
-
Solid understanding of distributed systems columnar databases and parallel data processing
-
Familiarity with cloud-based architectures (AWS GCP or Azure) and containerized infrastructure
-
Strong skills in data quality monitoring lineage and observability
-
Excellent communication and collaboration abilities particularly in cross-functional or client-facing environments
-
Bonus: Experience with multiple time series systems or contributions to open-source data infrastructure projects
Position: Senior Data Engineer Time Series Systems
We are looking for an experienced Senior Data Engineer to lead the design and development of high-performance scalable data infrastructure for large-scale time series workloads. This role is ideal for someone passionate about building robust systems that power real-time analytics and machine learning.
Key Responsibilities:
-
Design build and optimize data pipelines to process large volumes of time series data efficiently
-
Develop scalable data infrastructure using time series-focused technologies such as KDB TimeSet or Kronos
-
Qualifications:
-
5 years of experience in data engineering with a strong focus on large-scale and high-throughput systems
-
Hands-on experience with time series databases like KDB TimeSet or Kronos
-
Proven track record building batch and streaming data pipelines using technologies such as Apache Kafka Spark Flink or AWS Glue
-
Proficiency in Python with experience integrating data pipelines into ML workflows using libraries like pandas NumPy scikit-learn or PyTorch
-
Expertise in designing efficient data models and partitioning strategies for time series data
-
Solid understanding of distributed systems columnar databases and parallel data processing
-
Familiarity with cloud-based architectures (AWS GCP or Azure) and containerized infrastructure
-
Strong skills in data quality monitoring lineage and observability
-
Excellent communication and collaboration abilities particularly in cross-functional or client-facing environments
-
Bonus: Experience with multiple time series systems or contributions to open-source data infrastructure projects
#dataengineer
-
Create robust ingestion and transformation workflows to handle both real-time and historical datasets
-
Integrate time series systems with Python-based ML pipelines to support training and inference workflows
-
Collaborate closely with data scientists and ML engineers to ensure high-quality accessible data for experimentation and production
-
Design data models and schemas tailored for time series use cases supporting efficient downsampling indexing and aggregation
-
Monitor and optimize systems for performance reliability and scalability
-
Establish best practices in data governance lineage and observability within large-scale environments
-
Mentor junior team members in distributed processing data architecture and real-time systems
-
Work cross-functionally with product infrastructure and engineering teams to align data capabilities with business objectives
Employment Type
Full Time
